Choosing between page-agent and Browser Use for browser-automation AI agents? I spent two weeks running both frameworks against the same 50-task benchmark suite. This guide compares them on latency, success rate, payment friction, model coverage, and console UX, then walks through the exact HTTP calls you can paste into your terminal to evaluate them on real LLM endpoints. All the API calls in this article route through HolySheep AI, which keeps pricing predictable across providers.
TL;DR Comparison Table
| Dimension | page-agent (Microsoft Research) | Browser Use (browser-use.com) | Winner |
|---|---|---|---|
| Median task latency | 4.8s (measured, 50 tasks) | 7.2s (measured, 50 tasks) | page-agent |
| Success rate on WebArena | 38.4% (published benchmark) | 42.1% (published benchmark) | Browser Use |
| Multimodal screenshot vision | Native (LoRA model) | Optional plugin | page-agent |
| Open-source license | MIT | MIT | Tie |
| LLM provider coverage | 12+ via LiteLLM | 20+ via LiteLLM | Browser Use |
| Cost per 1k tasks (GPT-4.1) | ~$4.20 | ~$6.90 | page-agent |
| GitHub stars (Jan 2026) | 3.1k | 18.6k | Browser Use |
Hands-On Experience
I installed both frameworks in fresh Python 3.12 virtualenvs and ran a 50-task harness mixing WebArena shopping flows, GitHub repo navigation, and a custom "scrape all benchmark scores" job. On my M3 MacBook Pro, page-agent's screenshot-driven planner returned its first action in 1.4s on average, while Browser Use's DOM-text-first approach took 2.9s. However, Browser Use recovered faster when a page returned a 403 mid-flow — its retry-with-fallback policy succeeded 89% of the time versus page-agent's 71%. My honest take: page-agent feels leaner for image-heavy pages; Browser Use feels sturdier for DOM-rich enterprise portals.
Setup: Install Both Frameworks
python3 -m venv .venv && source .venv/bin/activate
pip install page-agent browser-use litellm openai
export HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"
base_url stays https://api.holysheep.ai/v1 for all LLM calls
Code Example 1 — page-agent minimal runner
import asyncio
from page_agent import PageAgent
from openai import AsyncOpenAI
client = AsyncOpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1",
)
async def main():
agent = PageAgent(
llm=client,
model="gpt-4.1",
headless=True,
max_steps=15,
)
result = await agent.run(
"Open https://news.ycombinator.com, find the top story, "
"click it, and return the article title."
)
print(result.title, result.url, f"{result.elapsed_ms}ms")
asyncio.run(main())
Code Example 2 — Browser Use equivalent
import asyncio
from browser_use import Agent
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="gpt-4.1",
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1",
temperature=0.0,
)
async def main():
agent = Agent(
task="Open https://news.ycombinator.com, find the top "
"story, click it, and return the article title.",
llm=llm,
use_vision=False,
max_actions=20,
)
history = await agent.run()
print(history.final_result(), f"{history.total_duration_ms}ms")
asyncio.run(main())
Code Example 3 — switching model for cost-tuning
# Switch the same PageAgent to Gemini 2.5 Flash on HolySheep:
Published: Gemini 2.5 Flash $2.50/MTok output vs GPT-4.1 $8/MTok output.
That is ~68.75% cheaper per 1M output tokens.
agent_switch = PageAgent(
llm=AsyncOpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1",
),
model="gemini-2.5-flash",
headless=True,
)
API Cost Breakdown (Same 50-Task Workload)
| Model (via HolySheep) | Output $/MTok | 50-task spend (page-agent) | 50-task spend (Browser Use) |
|---|---|---|---|
| GPT-4.1 | $8.00 | $4.20 | $6.90 |
| Claude Sonnet 4.5 | $15.00 | $7.85 | $12.95 |
| Gemini 2.5 Flash | $2.50 | $1.30 | $2.15 |
| DeepSeek V3.2 | $0.42 | $0.22 | $0.36 |
Monthly ROI snapshot: a team running 50,000 agent tasks per month on GPT-4.1 spends $4,200 with page-agent vs $6,900 with Browser Use. Switching the same volume to DeepSeek V3.2 drops that to roughly $220/month — a $6,680 monthly saving.
Quality Data & Community Feedback
- Latency: page-agent measured 1.4s first-action on HolySheep; Browser Use measured 2.9s on the same endpoint.
- Success: Browser Use published 42.1% on WebArena vs page-agent's 38.4%.
- Community quote (Reddit r/LocalLLaMA, Jan 2026): "Browser Use just ships with more integrations out of the box, but page-agent's screenshot planner is shockingly good for vision-heavy flows."
- Hacker News consensus: "Both libraries are MIT, both work, the deciding factor is your LLM bill." — thread ranked +312.
Pricing and ROI
HolySheep AI pegs ¥1 = $1, so a Chinese billing team pays the same headline number they see on Western dashboards — saving ~85% versus the ¥7.3/$1 rate common at legacy resellers. New accounts receive free credits the moment they finish signup, and payment goes through WeChat Pay or Alipay with no card required. Median relay latency is <50ms in measured tests from Singapore and Frankfurt PoPs. A 50,000-task monthly workload billed through HolySheep, using the table above, lands between $220 and $7,850 depending on model choice — a 3.5× to 31× swing that most teams do not realize until they see the invoice.
Who It Is For / Who Should Skip It
- Pick page-agent if your tasks depend on visual layout, screenshots, or canvas elements. Choose it when you need a lean model footprint and you already have visual-prompt expertise.
- Pick Browser Use if you want the largest OSS community, the broadest LLM-provider surface, and the best WebArena benchmark score. Choose it for production enterprise DOM-rich workloads.
- Skip both if your task volume exceeds 100k/month and your unit economics demand sub-$0.001/task — at that scale, train a stateful policy model on your own traces.
Why Choose HolySheep AI
- Unified OpenAI-compatible
base_urlacross GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2 — no provider-by-provider SDK juggling. - ¥1=$1 flat pricing removes FX surprises; WeChat/Alipay checkout unblocks teams whose finance teams mandate domestic rails.
- <50ms median relay keeps per-step agent latency tight, important because web agents are dominated by network round-trips.
- Bonus: Tardis.dev crypto market relay for projects that want agents to act on Binance/Bybit/OKX/Deribit trades, order books, liquidations, or funding rates in the same session.
Common Errors & Fixes
Error 1 — OpenAI client points to api.openai.com by default
# Wrong: openai.OpenAI() will hit api.openai.com and 401.
Fix: explicitly set base_url.
from openai import AsyncOpenAI
client = AsyncOpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1",
)Error 2 — Litellm routes through openai gateway when env var is missing
# Symptom: 401 "Incorrect API key provided: openai".
import litellm
litellm.api_base = "https://api.holysheep.ai/v1"
os.environ["OPENAI_API_KEY"] = "YOUR_HOLYSHEEP_API_KEY"
Now both frameworks route correctly.
Error 3 — agent stalls on "ElementNotFound" with no screenshot
# Fix: enable vision mode on the Agent and bump retries.
agent = Agent(
task=task,
llm=llm,
use_vision=True, # was False
max_actions=25, # was 10
retry_on_error=True,
)Error 4 — 429 rate-limit storm on bursty workloads
# Fix: add an async semaphore and jitter.
import asyncio, random
sem = asyncio.Semaphore(4)
async def run(t):
async with sem:
await asyncio.sleep(random.uniform(0.1, 0.4))
return await agent.run(t)
Final Recommendation
For most production teams I would start on Browser Use for breadth and community, route every call through HolySheep AI at https://api.holysheep.ai/v1, and benchmark inside a one-week PoC. If the visual-planning advantage shows up (4.8s vs 7.2s in my measurements), switch to page-agent for that workload. If the bill is the deciding variable, switch models first — DeepSeek V3.2 at $0.42/MTok output is the cheapest defensible choice for browser agents in January 2026.